计算机科学 ›› 2022, Vol. 49 ›› Issue (2): 304-311.doi: 10.11896/jsjkx.210100157

• 计算机网络 • 上一篇    下一篇

基于NOMA-MEC的车联网任务卸载、迁移与缓存策略

张海波, 张益峰, 刘开健   

  1. 重庆邮电大学通信与信息工程学院 重庆400065
  • 收稿日期:2021-01-21 修回日期:2021-05-06 出版日期:2022-02-15 发布日期:2022-02-23
  • 通讯作者: 张益峰(1582042470@qq.com)
  • 作者简介:zhanghb@cqupt.edu.cn
  • 基金资助:
    国家自然科学基金(61801065,61601071);长江学者和创新团队发展计划基金资助项目(IRT16R72);重庆市基础与前沿项目(cstc2018jcyjAX0463);重庆市留创计划创新类资助项目(cx2020059)

Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC

ZHANG Hai-bo, ZHANG Yi-feng, LIU Kai-jian   

  1. School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China
  • Received:2021-01-21 Revised:2021-05-06 Online:2022-02-15 Published:2022-02-23
  • About author:ZHANG Hai-bo,born in 1979,Ph.D,associate professor.His main research interests include vehicular networks and edge computing.
    ZHANG Yi-feng,born in 1995,postgraduate.His main research interests include vehicular networks and edge computing
  • Supported by:
    National Natural Science Foundation of China (61801065,61601071),Program for Changjiang Scholars and Innovative Research Team in University (IRT16R72),General Project on Foundation and Cutting-Edge Research Plan of Chongqing (cstc2018jcyjAX0463),Chongqing Innovation and Entrepreneurship Project for Returned Chinese Scholars(cx2020059).

摘要: 在移动边缘计算(MEC)与非正交多路接入(NOMA)技术相结合的车联网系统中,针对用户处理计算密集型和时延敏感型任务时面临的高时延问题,提出了一种基于博弈论和Q学习的任务卸载、迁移与缓存优化策略。首先,对基于NOMA-MEC的车联网任务卸载时延、迁移时延与缓存时延进行建模;其次,采用合作博弈算法获得最优用户分组,以实现卸载时延优化;最后,为避免出现局部最优,通过Q学习算法优化用户分组中的迁移缓存联合时延。仿真结果表明,所提方案相比对比方案,能有效提升卸载效率并降低约22%~43%的任务时延。

关键词: 车联网, 非正交多路接入, 移动边缘计算

Abstract: In the internet of vehicles systems that combining mobile edge computing (MEC) with non-orthogonal multiple access (NOMA) technology,to solve the high latency problem when user processes computationally intensive and latency-sensitive task,a strategy of task offloading,migration and cache optimization based on game theory and Q learning is proposed.Firstly,the mo-del of offloading delay,migration delay and cache delay of the internet of vehicles task based on NOMA-MEC is established.Se-condly,we use the cooperative game method to obtain the optimal user group to optimize the offloading delay.Finally,in order to avoid local optima,the Q learning algorithm is utilized to optimize the joint delay of the migration cache in the user group.The simulation results show that compared with other solutions,the proposed algorithm can effectively improve the offloading efficiency and reduce the task delay by about 22% to 43%.

Key words: Mobile edge computing, NOMA, Vehicular network

中图分类号: 

  • TN929
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